{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def reduce_mem_usage_colwise(col):\n",
    "    numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "    col_type = col.dtypes\n",
    "    if col_type in numerics:\n",
    "        c_min = col.min()\n",
    "        c_max = col.max()\n",
    "        if str(col_type)[:3] == 'int':\n",
    "            if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                col = col.astype(np.int8)\n",
    "            elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                col = col.astype(np.int16)\n",
    "            elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                col = col.astype(np.int32)\n",
    "            elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                col = col.astype(np.int64)  \n",
    "        else:\n",
    "            if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                col = col.astype(np.float16)\n",
    "            elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                col = col.astype(np.float32)\n",
    "            else:\n",
    "                col = col.astype(np.float64)       \n",
    "    return col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "import lightgbm as lgb\n",
    "\n",
    "import multiprocessing as mp\n",
    "from functools import partial\n",
    "import gc\n",
    "import time\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19 s, sys: 2.16 s, total: 21.2 s\n",
      "Wall time: 21.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "DATA_PATH = 'Data/'\n",
    "OUTPUT_PATH = 'Output/'\n",
    "\n",
    "oof_prev = pd.read_csv(OUTPUT_PATH + 'oofs_lgbm.csv')\n",
    "preds_prev = pd.read_excel(OUTPUT_PATH + 'preds_lgbm.xlsx')\n",
    "\n",
    "train = pd.read_csv(DATA_PATH + 'train_data.csv')\n",
    "train_labels = pd.read_csv(DATA_PATH + 'train_labels.csv')\n",
    "test = pd.read_csv(DATA_PATH + 'test_data.csv')\n",
    "sample_sub = pd.read_csv(DATA_PATH + 'Sample Submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = pd.merge(train, train_labels, on = 'patient_id', how='left')\n",
    "df = pd.concat([train, test])\n",
    "df = df.drop_duplicates()\n",
    "comp_train = train_labels.copy()\n",
    "comp_test = sample_sub.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['made_payment'] = pd.qcut(df['patient_payment'], 400, duplicates='drop', labels=False).astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 29 s, sys: 2.64 s, total: 31.6 s\n",
      "Wall time: 31.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "for c in ['event_name', 'plan_type', 'specialty', 'made_payment']:\n",
    "    recency = df.groupby(c).apply(lambda x: x.groupby('patient_id')['event_time'].min()).unstack().T.reset_index()\n",
    "    recency.columns = [col + '_' + c if col != 'patient_id' else 'patient_id' for col in recency.columns]\n",
    "    comp_train = pd.merge(comp_train, recency, on = 'patient_id', how='left')\n",
    "    comp_test = pd.merge(comp_test, recency, on = 'patient_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "comp_train['prev_preds'] = oof_prev['outcome_flag']\n",
    "comp_test['prev_preds'] = preds_prev['outcome_flag']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "comp_train = comp_train.apply(lambda x: reduce_mem_usage_colwise(x))\n",
    "comp_test = comp_test.apply(lambda x: reduce_mem_usage_colwise(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14350"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del train, test, df, recency, oof_prev, preds_prev, train_labels, sample_sub\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "438\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "439"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ID_COL, TARGET_COL = 'patient_id', 'outcome_flag'\n",
    "features = [c for c in comp_train if c not in [ID_COL, TARGET_COL]]\n",
    "target = comp_train[TARGET_COL]\n",
    "features = comp_train[features].isnull().sum().sort_values(ascending=False)[420:].index.tolist()\n",
    "print(len(features))\n",
    "to_drop_cols = [c for c in comp_train.columns if c not in features]\n",
    "to_drop_cols.remove(ID_COL)\n",
    "comp_train.drop(to_drop_cols, axis=1, inplace=True)\n",
    "comp_test.drop(to_drop_cols, axis=1, inplace=True)\n",
    "comp_train.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "param = {'num_leaves': 5000,\n",
    "         'min_data_in_leaf': 220, \n",
    "         'objective':'binary',\n",
    "         'max_depth': -1,\n",
    "         'learning_rate': 0.005,\n",
    "         \"boosting\": \"gbdt\",\n",
    "         \"feature_fraction\": 0.22,\n",
    "         \"metric\": 'auc',\n",
    "         \"lambda_l1\": 2,\n",
    "         \"lambda_l2\": 0.6,\n",
    "         \"random_state\": 6,\n",
    "         \"verbosity\": -1,\n",
    "         'scale_pos_weight': 1.15}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold n°0\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.911273\tvalid_1's auc: 0.83652\n",
      "[800]\ttraining's auc: 0.940299\tvalid_1's auc: 0.848034\n",
      "[1200]\ttraining's auc: 0.962182\tvalid_1's auc: 0.856011\n",
      "[1600]\ttraining's auc: 0.977676\tvalid_1's auc: 0.862563\n",
      "[2000]\ttraining's auc: 0.987575\tvalid_1's auc: 0.866877\n",
      "[2400]\ttraining's auc: 0.993572\tvalid_1's auc: 0.869715\n",
      "[2800]\ttraining's auc: 0.996896\tvalid_1's auc: 0.870827\n",
      "[3200]\ttraining's auc: 0.99862\tvalid_1's auc: 0.871773\n",
      "[3600]\ttraining's auc: 0.999428\tvalid_1's auc: 0.872317\n",
      "[4000]\ttraining's auc: 0.999776\tvalid_1's auc: 0.872346\n",
      "[4400]\ttraining's auc: 0.999921\tvalid_1's auc: 0.872079\n",
      "Early stopping, best iteration is:\n",
      "[3968]\ttraining's auc: 0.999758\tvalid_1's auc: 0.872546\n",
      "fold n°1\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.911843\tvalid_1's auc: 0.848174\n",
      "[800]\ttraining's auc: 0.941108\tvalid_1's auc: 0.855254\n",
      "[1200]\ttraining's auc: 0.962534\tvalid_1's auc: 0.860944\n",
      "[1600]\ttraining's auc: 0.977602\tvalid_1's auc: 0.865312\n",
      "[2000]\ttraining's auc: 0.987334\tvalid_1's auc: 0.868852\n",
      "[2400]\ttraining's auc: 0.993381\tvalid_1's auc: 0.870133\n",
      "[2800]\ttraining's auc: 0.996775\tvalid_1's auc: 0.871288\n",
      "[3200]\ttraining's auc: 0.998551\tvalid_1's auc: 0.871824\n",
      "[3600]\ttraining's auc: 0.999386\tvalid_1's auc: 0.871334\n",
      "[4000]\ttraining's auc: 0.999772\tvalid_1's auc: 0.871512\n",
      "Early stopping, best iteration is:\n",
      "[3233]\ttraining's auc: 0.998643\tvalid_1's auc: 0.872071\n",
      "fold n°2\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.913569\tvalid_1's auc: 0.833473\n",
      "[800]\ttraining's auc: 0.942515\tvalid_1's auc: 0.839042\n",
      "[1200]\ttraining's auc: 0.963597\tvalid_1's auc: 0.84325\n",
      "[1600]\ttraining's auc: 0.97854\tvalid_1's auc: 0.844657\n",
      "[2000]\ttraining's auc: 0.988124\tvalid_1's auc: 0.845746\n",
      "[2400]\ttraining's auc: 0.993859\tvalid_1's auc: 0.846546\n",
      "[2800]\ttraining's auc: 0.997043\tvalid_1's auc: 0.846767\n",
      "[3200]\ttraining's auc: 0.998673\tvalid_1's auc: 0.846586\n",
      "[3600]\ttraining's auc: 0.999429\tvalid_1's auc: 0.846391\n",
      "Early stopping, best iteration is:\n",
      "[2964]\ttraining's auc: 0.997839\tvalid_1's auc: 0.847096\n",
      "fold n°3\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.912211\tvalid_1's auc: 0.83983\n",
      "[800]\ttraining's auc: 0.942312\tvalid_1's auc: 0.851508\n",
      "[1200]\ttraining's auc: 0.963649\tvalid_1's auc: 0.856317\n",
      "[1600]\ttraining's auc: 0.978547\tvalid_1's auc: 0.859218\n",
      "[2000]\ttraining's auc: 0.988054\tvalid_1's auc: 0.861081\n",
      "[2400]\ttraining's auc: 0.993784\tvalid_1's auc: 0.861686\n",
      "[2800]\ttraining's auc: 0.996949\tvalid_1's auc: 0.862219\n",
      "[3200]\ttraining's auc: 0.998599\tvalid_1's auc: 0.861978\n",
      "[3600]\ttraining's auc: 0.999392\tvalid_1's auc: 0.861763\n",
      "Early stopping, best iteration is:\n",
      "[2935]\ttraining's auc: 0.997633\tvalid_1's auc: 0.862477\n",
      "fold n°4\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.91247\tvalid_1's auc: 0.83169\n",
      "[800]\ttraining's auc: 0.942171\tvalid_1's auc: 0.843115\n",
      "[1200]\ttraining's auc: 0.963843\tvalid_1's auc: 0.848034\n",
      "[1600]\ttraining's auc: 0.978772\tvalid_1's auc: 0.851906\n",
      "[2000]\ttraining's auc: 0.988268\tvalid_1's auc: 0.854179\n",
      "[2400]\ttraining's auc: 0.993839\tvalid_1's auc: 0.855469\n",
      "[2800]\ttraining's auc: 0.996981\tvalid_1's auc: 0.855354\n",
      "[3200]\ttraining's auc: 0.998634\tvalid_1's auc: 0.8555\n",
      "[3600]\ttraining's auc: 0.999417\tvalid_1's auc: 0.855317\n",
      "Early stopping, best iteration is:\n",
      "[3101]\ttraining's auc: 0.998333\tvalid_1's auc: 0.855787\n",
      "fold n°5\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.914076\tvalid_1's auc: 0.813477\n",
      "[800]\ttraining's auc: 0.942784\tvalid_1's auc: 0.8249\n",
      "[1200]\ttraining's auc: 0.963777\tvalid_1's auc: 0.831077\n",
      "[1600]\ttraining's auc: 0.978757\tvalid_1's auc: 0.835121\n",
      "[2000]\ttraining's auc: 0.988202\tvalid_1's auc: 0.836735\n",
      "[2400]\ttraining's auc: 0.99391\tvalid_1's auc: 0.837772\n",
      "[2800]\ttraining's auc: 0.997048\tvalid_1's auc: 0.83783\n",
      "[3200]\ttraining's auc: 0.998643\tvalid_1's auc: 0.83879\n",
      "[3600]\ttraining's auc: 0.999422\tvalid_1's auc: 0.839011\n",
      "[4000]\ttraining's auc: 0.999773\tvalid_1's auc: 0.838635\n",
      "Early stopping, best iteration is:\n",
      "[3483]\ttraining's auc: 0.999253\tvalid_1's auc: 0.839226\n",
      "fold n°6\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.913745\tvalid_1's auc: 0.847984\n",
      "[800]\ttraining's auc: 0.943066\tvalid_1's auc: 0.853773\n",
      "[1200]\ttraining's auc: 0.964417\tvalid_1's auc: 0.857645\n",
      "[1600]\ttraining's auc: 0.979198\tvalid_1's auc: 0.860748\n",
      "[2000]\ttraining's auc: 0.988619\tvalid_1's auc: 0.862446\n",
      "[2400]\ttraining's auc: 0.994137\tvalid_1's auc: 0.86335\n",
      "[2800]\ttraining's auc: 0.99717\tvalid_1's auc: 0.863353\n",
      "[3200]\ttraining's auc: 0.998715\tvalid_1's auc: 0.863361\n",
      "[3600]\ttraining's auc: 0.999456\tvalid_1's auc: 0.863407\n",
      "Early stopping, best iteration is:\n",
      "[3080]\ttraining's auc: 0.998347\tvalid_1's auc: 0.863766\n",
      "fold n°7\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.913223\tvalid_1's auc: 0.829447\n",
      "[800]\ttraining's auc: 0.942433\tvalid_1's auc: 0.840282\n",
      "[1200]\ttraining's auc: 0.963727\tvalid_1's auc: 0.848081\n",
      "[1600]\ttraining's auc: 0.978807\tvalid_1's auc: 0.85146\n",
      "[2000]\ttraining's auc: 0.988336\tvalid_1's auc: 0.854086\n",
      "[2400]\ttraining's auc: 0.994028\tvalid_1's auc: 0.855745\n",
      "[2800]\ttraining's auc: 0.997084\tvalid_1's auc: 0.856863\n",
      "[3200]\ttraining's auc: 0.998703\tvalid_1's auc: 0.857825\n",
      "[3600]\ttraining's auc: 0.999455\tvalid_1's auc: 0.858622\n",
      "[4000]\ttraining's auc: 0.99978\tvalid_1's auc: 0.859227\n",
      "[4400]\ttraining's auc: 0.99992\tvalid_1's auc: 0.859558\n",
      "[4800]\ttraining's auc: 0.999971\tvalid_1's auc: 0.85978\n",
      "[5200]\ttraining's auc: 0.99999\tvalid_1's auc: 0.859959\n",
      "[5600]\ttraining's auc: 0.999997\tvalid_1's auc: 0.860204\n",
      "[6000]\ttraining's auc: 0.999999\tvalid_1's auc: 0.860385\n",
      "[6400]\ttraining's auc: 1\tvalid_1's auc: 0.860575\n",
      "[6800]\ttraining's auc: 1\tvalid_1's auc: 0.860587\n",
      "[7200]\ttraining's auc: 1\tvalid_1's auc: 0.86031\n",
      "Early stopping, best iteration is:\n",
      "[6503]\ttraining's auc: 1\tvalid_1's auc: 0.860719\n",
      "fold n°8\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.91295\tvalid_1's auc: 0.846817\n",
      "[800]\ttraining's auc: 0.942453\tvalid_1's auc: 0.860284\n",
      "[1200]\ttraining's auc: 0.963964\tvalid_1's auc: 0.867464\n",
      "[1600]\ttraining's auc: 0.979113\tvalid_1's auc: 0.871289\n",
      "[2000]\ttraining's auc: 0.988556\tvalid_1's auc: 0.873461\n",
      "[2400]\ttraining's auc: 0.994214\tvalid_1's auc: 0.874362\n",
      "[2800]\ttraining's auc: 0.997285\tvalid_1's auc: 0.875033\n",
      "[3200]\ttraining's auc: 0.998788\tvalid_1's auc: 0.875045\n",
      "Early stopping, best iteration is:\n",
      "[2711]\ttraining's auc: 0.996774\tvalid_1's auc: 0.875295\n",
      "fold n°9\n",
      "Training until validation scores don't improve for 800 rounds\n",
      "[400]\ttraining's auc: 0.915759\tvalid_1's auc: 0.813676\n",
      "[800]\ttraining's auc: 0.944515\tvalid_1's auc: 0.82437\n",
      "[1200]\ttraining's auc: 0.965639\tvalid_1's auc: 0.831737\n",
      "[1600]\ttraining's auc: 0.980251\tvalid_1's auc: 0.836152\n",
      "[2000]\ttraining's auc: 0.989325\tvalid_1's auc: 0.838767\n",
      "[2400]\ttraining's auc: 0.994695\tvalid_1's auc: 0.840294\n",
      "[2800]\ttraining's auc: 0.997544\tvalid_1's auc: 0.840838\n",
      "[3200]\ttraining's auc: 0.99896\tvalid_1's auc: 0.841512\n",
      "[3600]\ttraining's auc: 0.999594\tvalid_1's auc: 0.842001\n",
      "[4000]\ttraining's auc: 0.999852\tvalid_1's auc: 0.841771\n",
      "[4400]\ttraining's auc: 0.999954\tvalid_1's auc: 0.841806\n",
      "[4800]\ttraining's auc: 0.999985\tvalid_1's auc: 0.841938\n",
      "[5200]\ttraining's auc: 0.999996\tvalid_1's auc: 0.842261\n",
      "[5600]\ttraining's auc: 0.999999\tvalid_1's auc: 0.842373\n",
      "[6000]\ttraining's auc: 1\tvalid_1's auc: 0.842278\n",
      "Early stopping, best iteration is:\n",
      "[5569]\ttraining's auc: 0.999999\tvalid_1's auc: 0.842436\n",
      "CV score: 0.85597 \n"
     ]
    }
   ],
   "source": [
    "max_iter = 10\n",
    "folds = StratifiedKFold(n_splits=max_iter, random_state=1991)\n",
    "oof = np.zeros(len(comp_train))\n",
    "categorical_columns = None\n",
    "predictions_test = np.zeros(len(comp_test))\n",
    "feature_importance_df = pd.DataFrame()\n",
    "\n",
    "for fold_, (trn_idx, val_idx) in enumerate(folds.split(comp_train.values, target)):\n",
    "    print(\"fold n°{}\".format(fold_))\n",
    "    X_trn, y_trn = comp_train.iloc[trn_idx][features], target.iloc[trn_idx]\n",
    "    X_val, y_val = comp_train.iloc[val_idx][features], target.iloc[val_idx]\n",
    "    X_comp_test = comp_test[features]\n",
    "    trn_data = lgb.Dataset(X_trn, y_trn, categorical_feature = categorical_columns)\n",
    "    val_data = lgb.Dataset(X_val, y_val, categorical_feature = categorical_columns)\n",
    "    num_round = 500000\n",
    "    clf = lgb.train(param, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=400, early_stopping_rounds = 800)\n",
    "    oof[val_idx] = clf.predict(X_val, num_iteration=clf.best_iteration)\n",
    "    \n",
    "    del trn_data, val_data, X_trn, X_val, y_trn, y_val\n",
    "    gc.collect()\n",
    "    \n",
    "    fold_importance_df = pd.DataFrame()\n",
    "    fold_importance_df[\"feature\"] = features\n",
    "    fold_importance_df[\"importance\"] = clf.feature_importance(importance_type='gain')\n",
    "    fold_importance_df[\"fold\"] = fold_ + 1\n",
    "    feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\n",
    "\n",
    "    current_comp_test_pred = clf.predict(X_comp_test, num_iteration=clf.best_iteration)\n",
    "    predictions_test += current_comp_test_pred / folds.n_splits\n",
    "    \n",
    "    del X_comp_test, current_comp_test_pred, clf\n",
    "    \n",
    "    gc.collect()\n",
    "  \n",
    "print(\"CV score: {:<8.5f}\".format(roc_auc_score(target, oof)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a88aba9e8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(predictions_test).plot(kind='density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a7e59aa90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fi = feature_importance_df.groupby('feature')['importance'].mean().sort_values()\n",
    "plt.figure(figsize=(10,10))\n",
    "fi[-20:].plot.barh(fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "oof_df = pd.DataFrame()\n",
    "oof_df['patient_id'] = comp_train['patient_id']\n",
    "oof_df[TARGET_COL] = oof\n",
    "oof_df.to_csv(OUTPUT_PATH + 'oof_lgbm_2.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>outcome_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>patient_2</td>\n",
       "      <td>0.065886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>patient_3</td>\n",
       "      <td>0.187469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>patient_5</td>\n",
       "      <td>0.559389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>patient_8</td>\n",
       "      <td>0.006929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>patient_14</td>\n",
       "      <td>0.449431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>patient_15</td>\n",
       "      <td>0.080379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>patient_16</td>\n",
       "      <td>0.015987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>patient_33</td>\n",
       "      <td>0.226737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>patient_38</td>\n",
       "      <td>0.012341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>patient_41</td>\n",
       "      <td>0.068882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id  outcome_flag\n",
       "0   patient_2      0.065886\n",
       "1   patient_3      0.187469\n",
       "2   patient_5      0.559389\n",
       "3   patient_8      0.006929\n",
       "4  patient_14      0.449431\n",
       "5  patient_15      0.080379\n",
       "6  patient_16      0.015987\n",
       "7  patient_33      0.226737\n",
       "8  patient_38      0.012341\n",
       "9  patient_41      0.068882"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df = pd.DataFrame()\n",
    "sub_df[ID_COL] = comp_test[ID_COL]\n",
    "sub_df[TARGET_COL] = predictions_test\n",
    "sub_df[[TARGET_COL]].to_excel(OUTPUT_PATH + 'preds_lgbm_2.xlsx', index=False)\n",
    "sub_df.head(10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
